Making large-scale support vector machine learning practical
Advances in kernel methods
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Discovering cluster-based local outliers
Pattern Recognition Letters
Estimating the Support of a High-Dimensional Distribution
Neural Computation
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Overview of intrusion detection and intrusion prevention
Proceedings of the 5th annual conference on Information security curriculum development
A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Large margin transductive transfer learning
Proceedings of the 18th ACM conference on Information and knowledge management
Cost-sensitive supported vector learning to rank imbalanced data set
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Learning with Minimum Supervision: A General Framework for Transductive Transfer Learning
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
An Ensemble Model for Mobile Device based Arrhythmia Detection
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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Detecting Electrocardiogram (ECG) abnormalities is the process of identifying irregular cardiac activities which may lead to severe heart damage or even sudden death. Due to the rapid development of cyberphysic systems and health informatics, embedding the function of ECG abnormality detection to various devices for real time monitoring has attracted more and more interest in the past few years. The existing machine learning and pattern recognition techniques developed for this purpose usually require sufficient labeled training data for each user. However, obtaining such supervised information is difficult, which makes the proposed ECG monitoring function unrealistic. To tackle the problem, we take advantage of existing well labeled ECG signals and propose a transductive transfer learning framework for the detection of abnormalities in ECG. In our model, unsupervised signals from target users are classified with knowledge transferred from the supervised source signals. In the experimental evaluation, we implemented our method on the MIT-BIH Arrhythmias Dataset and compared it with both anomaly detection and transductive learning baseline approaches. Extensive experiments show that our proposed algorithm remarkably outperforms all the compared methods, proving the effectiveness of it in detecting ECG abnormalities.